Static energy analysis of MHC class I and class II peptide-binding affinity

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

T-cell epitope prediction and immune complex simulation using molecular dynamics:state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

An insight into the activation mechanism of the calcatonin-gene-related peptide receptor

The calcitonin-gene- related peptide (CGRP) receptor is unique among G-protein coupled receptors (GPCRs) as it consists of at least three proteins: calcitonin receptor like receptor (CLR), receptor activity modifying protein (RAMP)1 and receptor component protein (RCP). An endogenous agonist for this curious receptor is aCGRP, which is a sensory nerve-derived peptide made up of 37 amino acids. aCGRP acts as a potent vasodilator having pronounced effects on arterioles and capillaries. Understanding the pharmacodynamics of the CGRP receptor may have pharmaceutical benefit as the receptor has been associated with the onset of migraines and implicated in Raynauds syndrome. The primary aim of this thesis was to identify functionally important residues in the extracellular face of the CGRP receptor. Three areas of interest were selected including the extreme N-terminus of the CLR, extracellular loop 1 (ECL1) of the CLR and its associated transmembrane (TM) regions, and finally extracellular loop 3 (ECL3) of the CLR and its juxtamembrane regions. A site-directed mutagenesis (SDM) strategy was used to investigate these regions, primarily substituting the innate residues of CLR with alanine and assessing the mutation on multiple criteria including a functional cAMP assay, cell-surface expression, total expression, agonist-mediated internalisation and aCGRP binding. The results are interpreted and discussed taking into consideration contemporary concepts surrounding Secretin-like GPCRs. Moreover, the thesis also contains details of RAMP purification. Overall the thesis provides novel data that furthers insight into the complex phenomenon of CGRP receptor activation. Site-directed mutants have been identified that affect aCGRP binding, receptor signal transduction, the CLR/RAMP1 interface and the integrity of the protein complex structure.

Structure-function analysis of amino acid 74 of human RAMP1 and RAMP3 and its role in peptide interactions with adrenomedullin and calcitonin gene-related peptide receptors

The receptors for calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) are complexes of the calcitonin receptor-like receptor (CLR) and receptor activity-modifying proteins (RAMP). The CGRP receptor is a CLR/RAMP1 pairing whereas CLR/RAMP2 and CLR/RAMP3 constitute two subtypes of AM receptor: AM(1) and AM(2), respectively. Previous studies identified Glu74 in RAMP3 to be important for AM binding and potency. To further understand the importance of this residue and its equivalent in RAMP1 (Trp74) we substituted the native amino acids with several others. In RAMP3, these were Trp, Phe, Tyr, Ala, Ser, Thr, Arg and Asn; in RAMP1, Glu, Phe, Tyr, Ala and Asn substitutions were made. The mutant RAMPs were co-expressed with CLR in Cos7 cells; receptor function in response to AM, AM(2)/intermedin and CGRP was measured in a cAMP assay and cell surface expression was determined by ELISA. Phe reduced AM potency in RAMP3 but had no effect in RAMP1. In contrast, Tyr had no effect in RAMP3 but enhanced AM potency in RAMP1. Most other substitutions had a small effect on AM potency in both receptors whereas there was little impact on CGRP or AM(2) potency. Overall, these data suggest that the geometry and charge of the residue at position 74 contribute to how AM interacts with the AM(2) and CGRP receptors and confirms the role of this position in dictating differential AM pharmacology at the AM(2) and CGRP receptors.

Regulation of signal transduction by calcitonin gene-related peptide receptors

Calcitonin gene-related peptide (CGRP) plays a pivotal role in migraine, activating its cognate receptor to initiate intracellular signalling. This atypical receptor comprises a distinct assembly, made up of a G protein-coupled receptor (GPCR), a single transmembrane protein, and an additional protein that is required for Ga(s) coupling. By altering the expression of individual receptor components, it might be possible to adjust cellular sensitivity to CGRP. In recognition of the increasing clinical significance of CGRP receptors, it is timely to review the signalling pathways that might be controlled by this receptor, how the activity of the receptor itself is regulated, and our current understanding of the molecular mechanisms involved in these processes. Like many GPCRs, the CGRP receptor appears to be promiscuous, potentially coupling to several G proteins and intracellular pathways. Their precise composition is likely to be cell type-dependent, and much work is needed to ascertain their physiological significance.

Proteins accessible to immune surveillance show significant T-cell epitope depletion:implications for vaccine design

T cell activation is the final step in a complex pathway through which pathogen-derived peptide fragments can elicit an immune response. For it to occur, peptides must form stable complexes with Major Histocompatibility Complex (MHC) molecules and be presented on the cell surface. Computational predictors of MHC binding are often used within in silico vaccine design pathways. We have previously shown that, paradoxically, most bacterial proteins known experimentally to elicit an immune response in disease models are depleted in peptides predicted to bind to human MHC alleles. The results presented here, derived using software proven through benchmarking to be the most accurate currently available, show that vaccine antigens contain fewer predicted MHC-binding peptides than control bacterial proteins from almost all subcellular locations with the exception of cell wall and some cytoplasmic proteins. This effect is too large to be explained from the undoubted lack of precision of the software or from the amino acid composition of the antigens. Instead, we propose that pathogens have evolved under the influence of the host immune system so that surface proteins are depleted in potential MHC-binding peptides, and suggest that identification of a protein likely to contain a single immuno-dominant epitope is likely to be a productive strategy for vaccine design.

Role of the transgenic human thyrotropin receptor A-subunit in thyroiditis induced by A-subunit immunization and regulatory T cell depletion

Transgenic BALB/c mice that express intrathyroidal human thyroid stimulating hormone receptor (TSHR) A-subunit, unlike wild-type (WT) littermates, develop thyroid lymphocytic infiltration and spreading to other thyroid autoantigens after T regulatory cell (Treg) depletion and immunization with human thyrotropin receptor (hTSHR) adenovirus. To determine if this process involves intramolecular epitope spreading, we studied antibody and T cell recognition of TSHR ectodomain peptides (A–Z). In transgenic and WT mice, regardless of Treg depletion, TSHR antibodies bound predominantly to N-terminal peptide A and much less to a few downstream peptides. After Treg depletion, splenocytes from WT mice responded to peptides C, D and J (all in the A-subunit), but transgenic splenocytes recognized only peptide D. Because CD4+ T cells are critical for thyroid lymphocytic infiltration, amino acid sequences of these peptides were examined for in silico binding to BALB/c major histocompatibility complex class II (IA–d). High affinity subsequences (inhibitory concentration of 50% < 50 nm) are present in peptides C and D (not J) of the hTSHR and mouse TSHR equivalents. These data probably explain why transgenic splenocytes do not recognize peptide J. Mouse TSHR mRNA levels are comparable in transgenic and WT thyroids, but only transgenics have human A-subunit mRNA. Transgenic mice can present mouse TSHR and human A-subunit-derived peptides. However, WT mice can present only mouse TSHR, and two to four amino acid species differences may preclude recognition by CD4+ T cells activated by hTSHR-adenovirus. Overall, thyroid lymphocytic infiltration in the transgenic mice is unrelated to epitopic spreading but involves human A-subunit peptides for recognition by T cells activated using the hTSHR.

The role of TG2 in regulating S100A4-mediated mammary tumour cell migration

The importance of S100A4, a Ca2+-binding protein, in mediating tumour cell migration, both intracellularly and extracellularly, is well documented. Tissue transglutaminase (TG2) a Ca2+-dependent protein crosslinking enzyme, has also been shown to enhance cell migration. Here by using the well characterised non-metastatic rat mammary R37 cells (transfected with empty vector) and highly metastatic KP1 cells (R37 cells transfected with S100A4), we demonstrate that inhibition of TG2 either by TG2 inhibitors or transfection of cells with TG2 shRNA block S100A4-accelerated cell migration in the KP1cells and in R37 cells treated with exogenous S100A4. Cell migration was also blocked by the treatment with the non-cell permeabilizing TG2 inhibitor R294, in the human breast cancer cell line MDA-MB-231 (Clone 16, which has a high level of TG2 expression). Inhibition was paralleled by a decrease in S100A4 polymer formation. co-immunoprecipitation and Far Western blotting assays and cross-linking assays showed not only the direct interaction between TG2 and S100A4, but also confirmed S100A4 as a substrate for TG2. Using specific functional blocking antibodies, a targeting peptide and a recombinant protein as a competitive treatment, we revealed the involvement of syndecan-4 and a5ß1 integrin co-signalling pathways linked by activation of PKCa in this TG2 and S100A4-mediated cell migration. We propose a mechanism for TG2-regulated S100A4-related mediated cell migration, which is dependent on TG2 crosslinking.

Mapping interaction sites within the N-terminus of the calcitonin gene-related peptide receptor; the role of residues 23-60 of the calcitonin receptor-like receptor

The calcitonin receptor-like receptor (CLR) acts as a receptor for the calcitonin gene-related peptide (CGRP) but in order to recognize CGRP, it must form a complex with an accessory protein, receptor activity modifying protein 1 (RAMP1). Identifying the protein/protein and protein/ligand interfaces in this unusual complex would aid drug design. The role of the extreme N-terminus of CLR (Glu23-Ala60) was examined by an alanine scan and the results were interpreted with the help of a molecular model. The potency of CGRP at stimulating cAMP production was reduced at Leu41Ala, Gln45Ala, Cys48Ala and Tyr49Ala; furthermore, CGRP-induced receptor internalization at all of these receptors was also impaired. Ile32Ala, Gly35Ala and Thr37Ala all increased CGRP potency. CGRP specific binding was abolished at Leu41Ala, Ala44Leu, Cys48Ala and Tyr49Ala. There was significant impairment of cell surface expression of Gln45Ala, Cys48Ala and Tyr49Ala. Cys48 takes part in a highly conserved disulfide bond and is probably needed for correct folding of CLR. The model suggests that Gln45 and Tyr49 mediate their effects by interacting with RAMP1 whereas Leu41 and Ala44 are likely to be involved in binding CGRP. Ile32, Gly35 and Thr37 form a separate cluster of residues which modulate CGRP binding. The results from this study may be applicable to other family B GPCRs which can associate with RAMPs.

Receptor activity-modifying protein-dependent effects of mutations in the calcitonin receptor-like receptor:implications for adrenomedullin and calcitonin gene-related peptide pharmacology

Background and Purpose Receptor activity-modifying proteins (RAMPs) define the pharmacology of the calcitonin receptor-like receptor (CLR). The interactions of the different RAMPs with this class B GPCR yield high-affinity calcitonin gene-related peptide (CGRP) or adrenomedullin (AM) receptors. However, the mechanism for this is unclear. Experimental Approach Guided by receptor models, we mutated residues in the N-terminal helix of CLR, RAMP2 and RAMP3 hypothesized to be involved in peptide interactions. These were assayed for cAMP production with AM, AM2 and CGRP together with their cell surface expression. Binding studies were also conducted for selected mutants. Key Results An important domain for peptide interactions on CLR from I32 to I52 was defined. Although I41 was universally important for binding and receptor function, the role of other residues depended on both ligand and RAMP. Peptide binding to CLR/RAMP3 involved a more restricted range of residues than that to CLR/RAMP1 or CLR/RAMP2. E101 of RAMP2 had a major role in AM interactions, and F111/W84 of RAMP2/3 was important with each peptide. Conclusions and Implications RAMP-dependent effects of CLR mutations suggest that the different RAMPs control accessibility of peptides to binding residues situated on the CLR N-terminus. RAMP3 appears to alter the role of specific residues at the CLR-RAMP interface compared with RAMP1 and RAMP2. © 2013 The Authors. British Journal of Pharmacology published by John Wiley &. Sons Ltd on behalf of The British Pharmacological Society.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Predicting class II MHC-Peptide binding:a kernel based approach using similarity scores

Background - Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. Results - The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database. Conclusion - The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Investigating G protein signalling bias at the glucagon-like peptide-1 receptor in yeast

Background and Purpose The glucagon-like peptide 1 (GLP-1) receptor performs an important role in glycaemic control, stimulating the release of insulin. It is an attractive target for treating type 2 diabetes. Recently, several reports of adverse side effects following prolonged use of GLP-1 receptor therapies have emerged: most likely due to an incomplete understanding of signalling complexities. Experimental Approach We describe the expression of the GLP-1 receptor in a panel of modified yeast strains that couple receptor activation to cell growth via single Gα/yeast chimeras. This assay enables the study of individual ligand-receptor G protein coupling preferences and the quantification of the effect of GLP-1 receptor ligands on G protein selectivity. Key Results The GLP-1 receptor functionally coupled to the chimeras representing the human Gαs, Gαi and Gαq subunits. Calculation of the dissociation constant for a receptor antagonist, exendin-3 revealed no significant difference between the two systems. We obtained previously unobserved differences in G protein signalling bias for clinically relevant therapeutic agents, liraglutide and exenatide; the latter displaying significant bias for the Gαi pathway. We extended the use of the system to investigate small-molecule allosteric compounds and the closely related glucagon receptor. Conclusions and Implications These results provide a better understanding of the molecular events involved in GLP-1 receptor pleiotropic signalling and establish the yeast platform as a robust tool to screen for more selective, efficacious compounds acting at this important class of receptors in the future. © 2014 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of The British Pharmacological Society.

Cardiac dysfunction and cell damage across clinical stages of severity in growth-restricted fetuses

Objective - The purpose of this study was to assess cardiac function and cell damage in intrauterine growth-restricted (IUGR) fetuses across clinical Doppler stages of deterioration. Study Design - One hundred twenty appropriate-for-gestational-age and 81 IUGR fetuses were classified in stages 1/2/3 according umbilical artery present/absent/reversed end-diastolic blood flow, respectively. Cardiac function was assessed by modified-myocardial performance index, early-to-late diastolic filling ratios, cardiac output, and cord blood B-type natriuretic peptide; myocardial cell damage was assessed by heart fatty acid–binding protein, troponin-I, and high-sensitivity C-reactive protein. Results - Modified-myocardial performance index, blood B-type natriuretic peptide, and early-to-late diastolic filling ratios were increased in a stage-dependent manner in IUGR fetuses, compared with appropriate-for-gestational-age fetuses. Heart fatty acid–binding protein levels were higher in IUGR fetuses at stage 3, compared with control fetuses. Cardiac output, troponin-I, and high-sensitivity C-reactive protein did not increase in IUGR fetuses at any stage. Conclusion - IUGR fetuses showed signs of cardiac dysfunction from early stages. Cardiac dysfunction deteriorates further with the progression of fetal compromise, together with the appearance of biochemical signs of cell damage.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).